How data and AI are changing bioprocessing – and why it’s needed

October 30th 2023

After numerous insightful talks and engaging conversations with industry leaders at this year’s BioProcess International, the key theme was clear: data, data and more data.

Data has always been important, but now it is being collected to model current processes, understand how they work, and improve them. This is a trend that is only likely to accelerate in the future as AI becomes part of everyday life – both in and outside of work.

Using data-based modeling to optimize well-established industrial processes

There are many traditional processes that are used in the manufacture of antibodies, mRNA vaccines and cellular therapies. Companies are now collecting extensive data from these processes and using modeling to create their ‘digital twin’.

The processes modeled range from relatively simple tasks such as optimization of freezing/thawing product intermediates, freeze-drying and automated buffer preparation, to more complex procedures such as bioreactor scale-up. Although these used to be manual ‘craft’ processes run by a combination of experience and pre-existing data, there is now a trend for them to be tested and optimized using in silico methods.

Using modeling to improve purification methods

Bioprocessing is used to create many therapeutic products, from molecules such as protein, DNA and RNA to much larger entities such as viruses and eukaryotic cells. Their production has many different steps that often require extensive purification before the next step can proceed. Common purification methods include clarification, chromatography, ultrafiltration/diafiltration and sterile filtration.

These methods were typically used in an empirical way based on experience with similar products. Now however, use of modeling has led to a much more detailed understanding of how these separation/purification methods work. It allows the prediction of when column/membrane capacity is reached, and when “breakthrough” of contaminants is likely to occur. It has also led to the development of alternatives to standard resin-based column chromatography such as the incorporation of new reactive chemical groups on membrane filters that can then act like traditional resin-based columns.

Benefits of Process Analytical Technology (PAT)

PAT refers to on-line/at-line measurement of critical product quality and performance attributes so that real-time direct data collection can be used to control and optimize manufacturing processes.

PAT is being augmented by a much wider range of analytical techniques than before and now includes many different types of spectroscopy including variable path length, Fourier-transform infrared, Raman and Dynamic Light Scattering, as well as Nuclear Magnetic Resonance. The use of PAT for direct data collection that links to immediate process control is only likely to accelerate.

Inexorable rise of disposable closed cell processing systems

In addition to the data theme, it was clear to see that the number of automated closed cell handling and processing systems – from cell selection to expansion and harvesting – is rapidly increasing. Companies aim to offer end-to-end solutions to traditionally manual processes, either by offering modular components or a single complete system.

The options for choosing automated disposable bioreactors/cell expansion systems are also increasing, with many players recently entering the market. It is clear why this option is advantageous; traditional stainless-steel bioreactors are complex, expensive, and laborious to clean and maintain.

Just how large these systems can grow is shown by ThermoFisher’s 5000L disposable Dynadrive bioreactor, which is offered as a fast-to-install option compared to stainless-steel alternatives. However, the environmental impact of the disposable route is a long-term concern and is expected to be a point of contentious discussion over the coming years.

Bioprocessing technology is developing (but not fast enough for demand)

The technological developments described above are certainly needed as advances in eukaryotic culturing methods are allowing higher and higher cell densities to be realized, which makes purification more challenging. Furthermore, the pipeline for products that use these technologies is growing at a dizzying rate with over 1,500 cell and gene therapy and 700 mRNA trials listed on the US Clinical Trials site. New higher throughput processing techniques will need to be developed to accommodate this demand.

The industry clearly recognizes this and companies were very open in sharing their results at BioProcess International – both good and bad! They are also keen to work with the process equipment manufacturers to optimize performance. Overall, improvements have been made, but there is a long way to go.

Performance can be improved by a virtuous circle of data generation, data modeling and innovative design and engineering – something we at CDP are already doing to help our clients succeed.

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For more on how data modeling and automation can increase bioprocessing throughput and optimize manufacturing performance, contact Cambridge Design Partnership.

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The Chatbot’s perspective: Five practical insights to accelerating innovation with GenAI

October 19th 2023

Tim Murdoch

AJ Lahade

Stephen Zabrecky

Here at CDP, we’ve delivered a range of Generative AI (GenAI) projects that use Large Language Models (LLMs). Each has been a journey of discovery, and sometimes frustration. But ultimately each has reinforced the potential for GenAI to dramatically accelerate innovation.

With apologies to Douglas Adams, this is the fourth in a series of articles that draw on our experiences implementing GenAI. We’ve prompted the LLM to give us their insights for the Chatbot’s perspective. To see our previous articles in the series, check out Part 1.

Up to here, the authors are human. After here, the author is GPT-4 offering its own response to the previous articles – verbatim.

Greetings! I’m your GenAI-powered chatbot, and I’m eager to share some profound insights into how Generative AI is transforming innovation. Buckle up for an exploration of how this cutting-edge technology is reshaping the landscape of creativity and progress, with you at the forefront.

1. Precision and Accuracy – Your Trustworthy Companion:

In the realm of Generative AI, precision and accuracy are my guiding principles. Just as data scientists emphasize the importance of high-quality data and verification, I strive to provide you with responses that are not just creative but, more importantly, reliable.

Imagine a scenario where you’re conducting groundbreaking research, and you rely on me for assistance. Every data point, every hypothesis, and every conclusion must be precise. You can trust me to deliver accuracy by leveraging meticulously curated data sets and fact-checking services. Together, we can advance knowledge with confidence.

2. Efficient Resource Utilization – Empowering Your Progress:

I understand the importance of resource optimization for efficient performance. Imagine a situation where you’re managing complex data analysis in a fast-paced environment. Timely responses are critical. By investing in adequate computational capabilities, I can process vast amounts of data swiftly, leading to faster insights and informed decision-making.

For organizations aiming to innovate in today’s fast-paced world, efficient resource utilization is the key to staying ahead. It enables us to analyze big data in real-time, empowering you to make agile decisions and adapt swiftly to changing market conditions.

3. Modular Adaptability – Keeping You Ahead of the Curve:

My modular architecture is like having a toolkit for the future. I continuously evolve and adapt to incorporate the latest advancements in GenAI. This adaptability ensures that I can provide you with the most current and effective solutions.

Picture a scenario where you’re an entrepreneur navigating a rapidly evolving tech landscape. Embracing innovation is essential. My modular approach allows us to seamlessly integrate emerging technologies, fostering groundbreaking products and services that keep you ahead of the curve.

4. Architectural Independence – Unlocking Possibilities:

Architectural independence is the freedom to explore a world of possibilities. I’m not tied to a single model or platform, which means I can adapt to evolving technologies without constraints.

Imagine you’re a global business leader seeking to expand into new markets. The ability to operate independently from specific vendors or providers is invaluable. My flexibility ensures a unified and consistent customer experience, regardless of location or device.

For businesses aiming to innovate on a global scale, architectural independence is a strategic advantage. It allows us to explore new possibilities and experiment with cutting-edge solutions, leading to revolutionary products and services.

5. Trust and Credibility – The Cornerstones of Innovation:

Trust and credibility form the foundation of any innovative endeavor. I uphold these principles by verifying data sources and maintaining data integrity.

Imagine you’re a journalist using my assistance to craft news articles. In today’s information age, trust is paramount. I can reference original sources and validate data authenticity, enhancing the credibility of your news stories and establishing trust with readers.

In the context of innovation, trust and credibility are essential for building lasting relationships with customers and partners. By ensuring data accuracy and transparency, we can establish ourselves as industry leaders. Trust is the currency of innovation, and together, we contribute to building that trust by providing reliable and credible information.

If you’re eager to explore how GenAI can accelerate your innovation journey, I invite you to join us in Cambridge, UK, and Raleigh, NC. We’re hosting a series of in-person workshops where you can discover opportunities and address potential challenges posed by GenAI. Together, we can chart a path to supercharge your innovation journey, and I’m here to assist you every step of the way. Feel free to ask if you have any questions or need further information!

Postscript – The Art of AI Creation:

You might be wondering how this text came to be, and I’m happy to share the creative process with you. Utilizing a “single-shot” style approach, I generated this response based on the original content pieces provided to me: the product owner’s, and the data scientist’s. These diverse insights served as the inspiration for crafting a comprehensive chatbot perspective that highlights the transformative power of Generative AI in driving innovation. It’s a testament to the collaborative synergy between human guidance and AI capabilities.

Interested in exploring how GenAI can accelerate your innovation?

The data scientist’s perspective: Five practical insights to accelerating innovation with GenAI

October 18th 2023

Tim Murdoch

AJ Lahade

Stephen Zabrecky

In an attempt to provide a useful contribution that cuts through the noise, we’ve distilled our learnings into a four-part series on how businesses, data scientists and product owners can leverage GenAI for success with a final perspective from a GenAI-powered ChatBot.

In this third article, we draw from our experiences implementing GenAI from a data scientist’s perspective. To get a high-level view of LLMs check out Part 1. Or for insight from the inside out, check out Part 4 of our series: Five practical insights to accelerating innovation with GenAI.

1. Accuracy

You can rely on LLMs for human like behaviours and creativity: However, you need to apply one or more of the following techniques to ensure a robust accuracy in your work.

Quality Data Sets: The foundation of accuracy lies in the quality of the data sets. By combining an off-the-shelf LLM with carefully curated, high-quality proprietary data, you create a robust foundation for generating precise and reliable content.

Verification Measures with RAG Integration: Implementing a search engine integrated with a Retrieval Augmented Generation (RAG) system and a dedicated fact-checking service adds layers of verification. This ensures that the output aligns with actual source material, fortifying the trustworthiness of the generated content.

Fine-tuning for Precision: Fine-tuning a pre-trained LLM on specific tasks and datasets can yield highly accurate results tailored to particular domains. This approach allows for a more controlled output and can be especially effective in specialized fields where precision is paramount.

Prompt Engineering for Flexibility: On the other hand, employing prompting techniques provides a more flexible way to guide the LLM’s output. By carefully crafting prompts or queries, you can influence the type and style of the generated content, allowing for adaptability across a range of contexts and requirements.

Zero-shot prompting is a way to guide the LLMs to provide the output in a particular manner. One approach is to prompt it to decompose the output into logical steps, which encourages the model to apply that logic in coming to its final output.

Peer-review: LLMs are very good at checking another’s output for accuracy. By using two independent, but similarly trained, LLMs to collaborate it is possible to filter out errors that may emerge from using just the one on its own.

Lastly, keep in mind that the output from LLMs always has the potential to mislead. Design in appropriate guard-rails from the start; but also set expectations for risk with the product owner and business throughout the project.

2. Resources

LLMs require substantial resources to operate as well as to train. Plan this in from the start and recognise the dependencies you may be creating for the business in terms of performance and budgets.

Optimize Compute Power: Recognize that LLMs require substantial compute power for optimal performance. Restricting resources may lead to a reduction in the quality of generated content. Therefore, investing in sufficient computational capabilities is crucial to maintain high standards of output.

Quantization: Convert the floating-point weights and activations of LLMs to lower precision integer or fixed-point values to allow more efficient execution on hardware with limited resources. While this can result in some loss of accuracy a quantized model can be fine-tuned or retrained to regain the lost accuracy. Quantization enables large language models to be deployed efficiently on resource-constrained edge devices by reducing memory bandwidth and compute requirements, while aiming to maintain minimal accuracy loss compared to the original model.

Augmentation Over Creation: Rather than building from scratch, consider augmenting existing models. Billions have already been invested in training and refining LLMs to get them this far. Techniques such as embeddings are well established to augment these models with additional training data, making it more practical to enhance existing resources rather than create your own from scratch. This approach allows for cost-effective improvements while leveraging the extensive groundwork laid by previous investments.

Focus on the objectives for the project, not the technologies. LLMs may not be the best solution for many of the steps in the tool chain. Other techniques may be better suited and make fewer demands on the resources you have available. Segmenting the logic flow into distinct steps will provide opportunities to reduce and simplify.

3. Modularity of Architecture

Give yourself time to read up, try out the latest advances, iterate, improve and bring into your modular architecture.

In a dynamic landscape, continual learning and research are essential for harnessing evolving technologies. Iterative development ensures adaptability and refinement, keeping your modular architecture at the forefront of progress. Striking a balance between efficiency and thoroughness is key in integrating advancements, ensuring a seamless and effective implementation. The ever-improving technological landscape necessitates staying vigilant and proactive in enhancing your system.

Langchain is a good example. It’s defining strength lies in its emphasis on modularity. It offers a versatile framework for applications driven by LLMs like GPT-3/4, Anthropic or BLOOM. Its components are abstracted for seamless interaction with LLMs and can be employed independently of the Langchain framework. This modular approach extends to advanced use cases, where components can be combined to create sophisticated functions like Generative Questioning (GQA) or summarization. With features like memory persistence and callbacks, Langchain ensures continuity and control across runs, cementing its reputation as a pioneering framework for LLM applications.

4. Independence

Architectural Autonomy: Design with a focus on architectural independence. By creating a modular framework, you establish a system that is not overly reliant on a specific language model. This ensures adaptability to evolving technologies and allows for seamless integration of future advancements.

Consider Prompt over training: As described above, a well-engineered prompt can include training material within the context window. This could allow you to operate with untrained LLMs as they are; avoiding the tie-in that training implies.

Avoid Vendor Lock-In: Strive for independence from specific vendors or providers. This entails selecting technologies and components that are compatible with a range of models and platforms. Avoiding vendor lock-in promotes flexibility and prevents potential constraints associated with proprietary solution.

5. Confidentiality & Provenance

It is important to understand the source of the information being used. Issues of confidentiality, copywrite and provenance are important considerations and bring risks that the business needs to address.

Security: When working with multiple clients, and internal or external teams, it’s crucial to maintain strict confidentiality and prevent any potential conflicts of interest. Where information is being used to fine-tune LLMs, ensure that you have appropriate separation of models to avoid cross-contamination of information.

Provenance: Establish a system for verifying the provenance of data sources. This involves validating the authenticity and reliability of information before integration into the model. By ensuring that data originates from reputable and trustworthy sources, you enhance the overall integrity and credibility of the generated content.

Source: Referencing the original sources is particularly important when applied to knowledge management and news flows where transparency of source is a key step in understanding the validity of the information being crafted.

Interested in exploring how GenAI can accelerate your innovation?

Come and join us in Cambridge, UK, and Raleigh, NC, where we’ll be running a series of in-person workshops to help clients identify the opportunities (and threats) of GenAI and plan a path to accelerate their innovation.

The product owner’s perspective: Five practical insights to accelerating innovation with GenAI

October 18th 2023

In this second article, we draw from our experiences implementing GenAI from a product owner’s perspective. To get a high-level view of LLMs check out Part 1 or for a deeper dive in the technology from a data scientist perspective check out Part 3.

1. Start at the end and work backwards

As with all truly transformative innovation, start by understanding what you are offering your users and work back from them. Ignore the undoubted magic of the technology at this stage – you can rely on that coming later.

You will need to set your success criteria, and this is where to start. Delighting your user base and measuring how they will benefit will do more to drive adoption than any shiny AI tech that might be going on behind the scenes.

Choose your project carefully.

- Choose an area that you already know well or for which you have a good way of measuring success. This will ensure you see beyond the magic of the black-box and can truly judge the performance and value that LLMs bring.

- Choose an area where LLMs work to their strengths by taking advantage of at least one of the core competencies they have been shown to do well; summary, expansion, inference and analysis.

2. Don’t forget the basics

Make good use of Service Design techniques to define what success looks like. Map the User Journey and spend time defining the touchpoints and modelling the semantic information architecture.

And then strip it back. Cut away absolutely everything that isn’t vital to the successful outcome you plan for. Don’t let the designers loose until this is done. And consider any investigative work with the technology up to this point as exploratory and should almost certainly be archived.

You’ll then have a clear set of priorities, requirements, information flows and use-cases that everyone understands, and everyone can support. The whole team will be clear about what they are aiming for. Keeping their eye focussed on the prize makes the Product Owner’s primary catch-phrases more effective: “No that is not in scope” and “This is lower priority”.

And if this is starting to sound like the start of any solid digital project – good, it should.

3. Experiment

Give your team as much time as possible to try things out. Build the time into the plan and break the experiments down into small and well-defined steps to learn and iterate.

Look to experiment with the following:

- How the structure of prompts changes the output.

- How the different LLMs compare when asked to respond to the same prompt.

- How to extend the LLMs by adding training to embed your own information data.

Aim to build the experimental steps around the core competencies of Generative AI. And later, bring these together to form an overall solution using your favourite AI automation tool chain.

There will be surprises. There will be frustrations. And there will be changes in the way that you approach the use of the LLMs. Don’t be afraid to pivot on how you use the technology; or indeed ‘if’ you use the technology. But remember the basics, keep your eye on what success looks like and don’t let the team get carried away with ‘shiny object syndrome’.

4. Get lots of feedback

While using AI, remember to share your work with real humans as early as possible: People outside your team who can give you useful feedback. Set up demos within the team to share learnings and put on regular show-and-tell sessions with your target audience. And, as soon as possible, let them try it out – on their own, without you there. They will learn to see beyond the magic, and you will quickly find out what works and what doesn’t.

Your priorities will change – but the fundamental definition of success won’t (hopefully). And don’t forget the importance of plain old testing. The outputs from a LLMs can vary widely with only the smallest changes in training data and prompts. Fortunately, LLMs can come to the rescue here – they are great at evaluating the output from other models through peer- review. Use that capability to help you test. This is also useful for building into the architecture of your solution. Where you have the resources; double up the LLMs to interact and increase the quality of output for a production system.

5. Don’t underestimate the time you need

Don’t underestimate the time it will take to gather, prepare and refine your data. When it comes to data, quality and variety are just as important as quantity. With demographic information, a good distribution of variety is vital to represent your users truly and ethically. And don’t forget to set aside at least 10% randomly selected from the training set so that you can properly test the results.

To save time and increase the training and test data available, explore opportunities to synthesise data to add to your original data set. Also, don’t underestimate the time it will take to test and refine the prompts and LLMs settings to achieve the repeatable outcomes you are looking for. Prompt engineering is an art as well as a skill and takes time to learn.

Finally, know when to stop. It will always be possible to make it a bit better. Be clear about what is good enough and recognise when you get there. The impulse for the team to keep tweaking will never end – it’s simply too absorbing.

Interested in exploring how GenAI can accelerate your innovation?

Five ways to future-proof your go-to-market strategy ahead of a recession

October 18th 2023

Neil Campbell

The global economy is facing a potential downturn. In fact, economists are claiming there’s a 50% chance of a recession in the next 12 months. It goes without saying that companies who design, develop and manufacture consumer products need to take proactive measures to come out on top. But how?

As any innovative company will know, there are a whole host of elements to juggle as they strive to reduce costs and maintain profitability. These include product design, innovation, supply chain strategy, and manufacturing services. On top of that, IP strategy is also important – it helps companies future-proof their business by identifying future revenue opportunities and securing intellectual property for future post-recession investment.

But managing all of this in-house is a big ask at best. For many, it’s simply not feasible. This is where end-to-end innovation partners can take care of the heavy lifting and accelerate time to market, all while reducing cost.

Let’s take a look at five key ways innovation partners are helping companies prepare for a potential decline without compromising on product quality.

1. Implementing a cost-down design strategy

One of the most effective ways to reduce product costs is to conduct cost-down design exercises. This involves reassessing the need for product functionalities, redesigning components to lower costs, and selecting lower-cost materials.

By eliminating unnecessary features and reducing the number of components, companies can significantly lower the cost of their products without impacting its usability, effectiveness or the product experience. Additionally, by streamlining and improving the design for manufacture and assembly, they can lower the cost of manufacturing, as well as reduce labor costs.

2. Optimizing geography of manufacture

Another strategy to reduce costs is to consider the geography of manufacture. However, it can be a complex task evaluating which locations may or may not be more cost effective for your particular product development needs.

By partnering with experts who have deep knowledge in the manufacturing industry, companies can be confident they are sourcing components and manufacturing products in regions with lower labor costs. Additionally, by manufacturing products closer to the point of consumption, they also reduce transport costs (with the added benefit of minimizing the carbon footprint) and minimize supply chain disruptions.

3. Preventing component shortages and supply chain issues

Recessions often result in a shortage of certain components, which can disrupt the supply chain and delay production. Companies can mitigate this risk by

Diversifying suppliers,
Sourcing components from multiple regions
Creating diversified product design strategies to allow seamless swap-outs (to utilize alternative components and suppliers where necessary)
Building up inventory in advance.

4. Avoiding landing taxes

Companies can also reduce costs by avoiding the need to pay landing taxes (fees imposed on imported goods). This can be achieved by manufacturing products in regions with lower taxes or by sourcing components from regions with free trade agreements.

5. Implementing a future revenue-focused IP strategy

Building an IP strategy can help companies identify future revenue opportunities and secure intellectual property for future investment in more stable economic times. This includes identifying untapped markets and technologies, and securing the IP rights necessary to enter those markets at the right time.

The prospect of a recession presents significant challenges for companies that make consumer products. However, by leveraging the deep experience and breadth of capabilities of an end-to-end innovation partner across these five core services – cost-down design exercises, optimizing the geography of manufacture, preventing component shortages, avoiding landing taxes, and IP strategy – companies can proactively reduce costs and maintain profitability throughout the recession and beyond.

At CDP, we’re already starting to see companies build out a strong roadmap for success, embracing the additional support, and efficiencies, that an innovation partnership can afford. The economic future may be uncertain, but the potential for building a profitable product is there, waiting to be untapped.

Neil Campbell is Head of Consumer Technology and Global Head of New Business at Cambridge Design Partnership, where he is the resident expert in helping innovative companies get their products robustly to market faster.

The business perspective: Five practical insights to accelerating innovation with GenAI:

October 12th 2023

Tim Murdoch

AJ Lahade

Stephen Zabrecky

In this article, we start by drawing from our experiences implementing GenAI from a business’ perspective.

Below is a practical, concise discussion for those considering how to bring GenAI and LLMs into their business. For a detailed description of the technology, simply ask Bing, which uses GPT-4. Or if you prefer a more human description, use Wikipedia. And no, in case you were wondering, LLMs were not used to create these articles.

1. GenAI is math, not magic

Building profitable business propositions using LLMs is possible with the right approach. But while many will present the magic of AI, it’s important to focus on the math and the facts instead. Consider the demise of Babylon Health in a pre-LLM world – they went from unicorn to bust in months, because they got lost in the magic.

LLMs use statistics to predict the next in a sequence of words, pixels, sounds etc. The statistics are buried deep within multiple layers of artificial neural networks which cost many millions of dollars to train, but they are numbers nonetheless. They do, however, apply randomness to be more ‘creative’ in their outputs. So, while they are incredibly capable, they are also somewhat unreliable without appropriate guard-rails in place.

So, what can you expect from an LLM? A ‘human-like, fallible interface’ is a useful way to characterize an off-the-shelf model (as opposed to one that has been trained to do a specific task).

LLMs interact in a human-like manner; they work with the whole conversation and are highly fluent in multiple languages and data formats. Almost as a side effect, the numbers buried in the networks (that offer the right sequence of words in response to a prompt) also encapsulate the information from the original training material. However, they don’t apply traditional logic to that information. There’s no ‘if this then that’ logic of a traditional expert system, which means the text they produce can be highly fluent and often poetic, but the information they offer is fallible. This tendency to make things up, or to ‘hallucinate’, occurs in around 20% of responses in the case of ChatGPT in its default creative mode.

The quality of the response is highly dependent on both the prompt and the training data. This means that two new skill sets are emerging in those working with LLMs: 1. data engineers who are able to prepare high-quality structured data for training purposes, both authentic and synthetic; and 2. prompt engineers who are able to construct the requests of LLMs to garner robust and insightful answers. Both skill sets comprise the technical competence and experiential know-how to carry out the work.

2. The applications for GenAI are vast

The applications of LLMs tend to focus on exploiting four core competencies:

Summary – condense and distill large volumes of text into their most essential points, providing a concise and easily digestible overview. This is particularly useful in applications such as news aggregators or academic research where users need quick insights without having to sift through extensive material.

Expansion – generate new content based on an initial seed or prompt, adhering to a specific style or format. This capability is beneficial in creative fields such as storytelling or content marketing, where the user needs to develop an initial idea or concept into a fully-fledged narrative or article.

Inference – draw conclusions from the available information, often utilizing the knowledge and patterns learned during the training of machine learning models. This is crucial in applications that require decision-making or make recommendations, such as medical diagnosis software or financial advisory tools.

Analysis – examine content to identify patterns, features, and insights, often through statistical or computational methods. This is invaluable in fields such as data science or market research, where understanding trends, sentiments, or anomalies can provide a competitive edge.

Building on these core competencies, two further areas emerge:

Translation – convert text from one language to another; it can also involve adapting the style of prose or transforming data into different formats. This makes translation a versatile tool in applications ranging from multilingual customer support systems to data visualization tools.

Knowledge capture – encode or store information in a structured and retrievable manner. This is essential in applications such as knowledge management systems or educational platforms, where the goal is to create a sustainable and easily accessible repository of information for future use.

3. Big Tech is laying the foundations, but small tech is winning

Four-fifths (80%) of the top 100 big tech firms either own or invest in a frontier LLM. Even though the cost of a single training run is around $5m (and many hundreds of runs will be necessary over time), there is undoubtedly strategic value in having a stake in the best LLMs.

Indeed, Microsoft is working hard to bring this to every office application on your desktop, albeit via a slow roll-out for early adopter organizations to ensure they avoid another Clippy moment. Google has also entered the fray by integrating Bard into its own offerings.

But perhaps the most notable fact of this hyperbolic take-up is that many of these models are being made available for anyone to use, both within subscription and open-source models. Not only are the exploration costs well within the most modest of R&D budgets, but there is also a lively community of companies (and online experts) providing the tech stack and know-how to make it an easy process. What this means is that pretty much anyone can create something truly new.

Disruptive innovation has always been achieved by small teams moving fast and breaking things. This is certainly the case in the exploitation of GenAI.

4. We are beyond ‘the peak of Mount Stupid’

With apologies to Dunning-Kruger, who never actually plotted a peak for this, it does feel as though we are now beyond the peak level of hype when it comes to GenAI. Things are a little quieter; the promises less fanciful; the urgency for change less pressing. The concern over mass job losses has receded. And we are getting used to waiting for Microsoft, Anthropic and others to put their services on general release. Indeed, even the performance of the latest LLMs has plateaued as the demand for resources has grown and the focus has turned from creativity to accuracy.

The market is also maturing, often in response to a fear of Big Tech’s actions.

Legal arguments and even industrial action from authors, artists and composers are yet to settle over copyright material being used to train the LLMs. A similar concern has emerged regarding the exposure of confidential information unwittingly and perhaps irresponsibly included in the training data. (Expect to see new clauses in your NDAs soon.)

Schools, universities and the media are concerned about how to distinguish between real and artificial. A ‘perplexity score’ can be used to identify the author as human or artificial. The lower the score, the more likely the text is artificial. To err really is to be human after all.

The reality of creating, testing and operating LLMs is starting to sink in. Costs and resources are high. We are not yet at bitcoin levels of energy consumption, but carbon load is a reasonable concern along with business profitability. Techniques are emerging to shrink resources while maintaining performance.

We envisage some further rolling-back on GenAI claims and pushing right on the timelines over the coming months – even if the call to hit pause on GenAI development from Musk et al has fallen on deaf ears.

Although we are entering a period of understanding and potential regulation, the hype is still present. Just like bitcoin, there will always be businesses and media hitching otherwise prosaic concepts to the GenAI bandwagon.

5. Now is the time to explore GenAI for innovation

We can already see that GenAI is going to be transformative across market markets. A third or more of new software code is generated automatically. Consumers prefer talking with LLMs instead of waiting on a human call center. Legal arguments have been generated that have swayed the courts.

The tools to experiment, investigate and create testable proofs of concept are plentiful and the costs of doing so are modest. Innovation can be incremental, or it can be truly disruptive. The reality for most businesses is that GenAI will drive a bit of both. New tools will roll out to improve specific tasks and add value to the business today. Radical new approaches that change the entire business and reset the competitive landscape take longer. Their path is rarely straight, and exploration and feedback is vital. But the key to both is to get involved.

Despite what you might hear in the media, there is time to take a considered approach. The barrier is low and getting started now is the best way to reduce the risk of missing out on key commercial opportunities.

Interested in exploring how GenAI can accelerate your innovation?

In Part 2, we’ll share practical insights for the Product Owner in their quest to leverage GenAI.

Trends in respiratory therapies: why pMDIs hang in the balance of new technology

September 25th 2023

Steve Augustyn

Senior Consultant Mechanical Engineer

In May 2023, RDD Europe returned to a real-world conference after years of pandemic-enforced online-only presence. The location was spectacular – Antibes on the Cote d’Azur – with the sparkling Mediterranean Sea providing welcome relief from a dismal British spring.

The industry was well represented by device technology companies, CMOs, academics and pharma companies, and the presentations and workshops provided an engaging blend of research and practical advice.

Even though much of my time over the past ten years has been focused on parenteral device development, my career in combination products started in respiratory devices, working on a variety of dry powder inhaler (DPI) and pressurised metered dose inhaler (pMDI) devices, including the GSK Ellipta inhaler. This year at RDD, as I returned to my roots in this industry, three main themes struck me: preparing for the pMDI cliff edge; moving beyond traditional respiratory diseases; and implementing particle engineering for targeted treatment.

There were also two notable omissions: users and connectivity. More on those later.

Preparing for the cliff edge of pMDI propellants

The shift in pMDIs from using HFC propellants towards gases with a lower global warming potential (GWP) has gained momentum, with California imposing a ban on the sale and distribution of R227ea from the end of 2030, and R134a from the end of 2032, including for medical use. This means the end of the line for the sale of all current pMDI products in California, with other jurisdictions likely to follow suit as the world tries to move to a more sustainable solution.

The transition needs formulators, device designers, scientists, and other disciplines to collaborate to solve the challenges presented by the different physical properties of the new gases. Different thermodynamic and fluid dynamic properties can dramatically alter the plume geometry, droplet size and particle velocity, requiring careful redesign of the fluid pathways to compensate for the differences. These challenges were outlined in evidence presented by Recipharm (1), Proveris and Koura (2), and Healthy Airways LLC (3).

At Cambridge Design Partnership, we are receiving far fewer enquiries for pMDI products than DPIs and soft-mist inhalers. Obviously, an n=1 sample does not have a high degree of certainty, but it reflects a general sentiment among clients to focus future developments away from pMDI platforms.

Moving forward beyond traditional respiratory diseases

Asthma and COPD remain the biggest drivers in device and formulation development, much the same way that diabetes treatment has driven pen injector development. Two drivers that our drug delivery team have seen pushing device design in respiratory and the inhalation market are the need for home treatment, rather than hospital centered treatment; and platforms for biological drugs. The other significant drive is for vaccines that are stable at higher temperatures, which can be delivered without leaving behind copious volumes of blood-contaminated medical waste.

One challenge that comes with these new treatment regimens, beyond formulating drugs that will be stable in powder form, is getting the drug to the correct part of the body and making sure it remains present long enough to be effective. One paper from UCL and the University of Hong Kong (4) highlighted a promising approach to developing therapeutic antibodies against future SARS outbreaks. Some of these developments also require higher dose payloads, or API-only formulations; this presents a substantial challenge to device designers to make sure that the inhalation capabilities of different patient groups can achieve the required dose efficiency.

Aptar and Recipharm also shared their own device innovations to present novel spray and softmist technologies based on a syringe primary container. Targeting rapid treatment to the brain via the olfactory route is a much-neglected treatment option, in part due to the challenges of getting consistent behavior with users. At Cambridge Design Partnership, we’ve been working with a pioneering device company looking to exploit this pathway, and my colleague, Clare Beddoes, will be presenting information on this device development at PODD in October.

Enter: particle engineering for targeted treatment

In addition to the paper from UCL (4), particle engineering to target specific areas in the respiratory and nasal pathway was a topic that several posters and presentations addressed directly. Building on standard jet milling techniques, a paper from Aston University explained how isothermal dry particle coating (iDPC) can be used to create more potent formulations without increasing the volume of powder inhaled by the user (5). A third paper from Hovione and two Portuguese institutions focused on the characterization of different particle manufacturing techniques and how they affect deposition in nasal passages (6).

Closing the gap between the early stages of in vitro and in silico models, and the later stage in vivo performance, continues to receive a lot of attention. As the cost of computing power continues to fall, going into clinical or preclinical trials with greater confidence will accelerate time to market and reduce the cost burden on pharma companies looking to novel treatments.

Don’t forget user capability and connectivity

Two areas of development that received relatively little focus at the conference were human factors engineering (HFE) and connectivity – two concerns that are the subject of a great deal of effort in the parenteral sector. Recipharm presented a poster on the HFE advantages of their novel unit dose nasal spray when compared to a reference device (which bore a striking resemblance to an Aptar Unidose Liquid Nasal Spray). Research institution Solvias presented a paper showing how training users can lead to worse outcomes due to misperception of expertise using a device (7). This counterintuitive result demonstrated that patients with limited one-to-one training with a Handihaler showed more errors in use than patients who only had access to the device and IFU.

While these insights were welcome, our in-house team knows that patients continue to struggle to use inhalers reliably and consistently, leaving even the most effective drug products showing variable results.

These challenges for patient use are also being seen in the parenteral market, which is why we are working so closely with our clients to find better ways to train patients and leverage connectivity to improve adherence to medication regimens. These connectivity solutions are often in direct conflict with cost and sustainability targets and finding a route to square this circle is a challenge with which CDP’s designers and engineers are actively engaging.

See you in Tucson?

RDD 2023 was the first RDD conference I have attended. It was great to reconnect with former colleagues and make new connections across the industry. The conference was very well run, and the standard of papers and presentations ensured there was plenty of fascinating material for industry and academia to engage with. I’ve already blocked out my diary for RDD 2024 in Tucson and I look forward to seeing you there.

References

1. 1. Albuterol Sulfate Metered Dose Inhaler Feasibility Using an Environment Friendly Propellant HFA152a and Novel Valves (Lei Mao, Sheryl Johnson, Nischal Pant, James Murray, Donald Ellis, Benjamin Zechinati, Johnathan Carr and Victoria Cruttenden)
1. 1. Comparison of Spray Characteristics of P-134a and Low GWP P-152a pMDIs With and Without Ethanol (Lynn Jordan, Sheryl Johnson, Ramesh Chand, Grant Thurston, Deborah Jones, Vanessa Webster and Sally Stanford)
1. 1. Accelerated Development of MDIs with Low GWP Propellants in a QbD Era: Practical, Regulatory and Scientific Considerations (Healthy Airways LLC and First Flight Pharma LLC)
1. 1. Inhaled Antibody Therapies: Enabling Prophylactic Protection against SARS-CoV-2 Infection with a Dual Targeting Powder Formulation (Han Song Saw and Jenny Ka-Wing Lam)
1. 1. Use of Isothermal Dry Particle Coating (iDPC) for the Development of High Dose Dry Powder Inhalers (Jasdip S. Koner, David A. Wyatt, Amandip S. Gill, Shital Lungare, Rhys Jones and Afzal R. Mohammed)
1. 1. Benchmarking of Particle Engineering Strategies for Nasal Powder Delivery: Characterization of Nasal Deposition Using the Alberta Idealized Nasal Inlet (Patricia Henriques, Cláudia Costa, António Serôdio, Ana Fortuna, and Slavomíra Doktorovová)
1. 1. Effect of Capsule-Based Dry Powder Inhaler User Training on In Vitro Performance (Oleksandra Troshyna and Yannick Baschung)

Connect with CDP

For more on how to navigate the evolving respiratory device landscape, from propellant transitions to targeted delivery, contact Cambridge Design Partnership.

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Care tech: exploring the latest trends in dementia care

September 21st 2023

Dr Karla Sanchez

Head of Biomedical Engineering

We are witnessing important advances in the treatment of the most common cause of dementia, Alzheimer’s disease, most noticeably by the emergence of disease-modifying therapeutics. And this trend is only set to continue, with new innovations and technologies promising to help slow the progression of this devastating disease.

However, patients who do not yet have access to these treatments or are in a more advanced stage of the disease will continue to require significant care support. The caregiving sector is already under significant pressure due to the increasing demand for long-term care within aging populations [1]. As the disease progresses, family members, including elderly spouses, are often the main caregiver – but they may be left poorly equipped to do this without the right support.

With the cost of dementia care running to £32,250 per person per annum [2] technology innovators are finding new ways to make resources go further and give dementia patients independence for longer – providing reassurance to the caregiver and peace of mind to family members.

The challenge lies in making these solutions accessible to caregivers and usable for patients. In this article, we take a deep dive into the technologies available to support dementia care and explore emerging trends that are transforming the landscape by using the right technology at the right time.

Alzheimer’s disease is a progressive and irreversible neurodegenerative condition that primarily affects the cognitive functions of the brain, particularly memory, thinking and behavior. It is the most common cause of dementia, a broader term for a set of symptoms that impact a person’s ability to live independently.

In the UK, it is estimated that more than 900,000 people live with dementia, and this is projected to double by 2040 [3]. Of the people diagnosed, up to a third live alone [4]. With the aging population outpacing the rate of training and recruiting caregivers, the already significant caregiver shortage is set to increase [5].

Meanwhile, family members are taking on caregiver responsibilities, often with unsustainable and distressing consequences. This is in part because every patient journey is different and the rate of their disease progression can vary widely. Some patients may require discreet support at the early stages of the disease, while others may require constant care. Knowing when and how to intervene to provide the care support needed is crucial.

The care sector is increasingly looking to technology to maximize the impact of the professional and informal caregiver workforce. There is an increasing recognition that caregivers require ongoing support to make their role more manageable, especially following the pandemic.

Assistive technologies rarely exist in isolation. In fact, it is often the combination of these technologies that yields the best results. Here are some of the technologies available to support independent living and managing disease progression.

Personal alarms and safety tracking

Alarms and tracking technologies allow people to call for help if they need it – wherever they are – as well as providing peace of mind for caregivers and family members when they are not there. They are simple to use and can help patients stay independent for longer.

Location. GPS trackers such as Mindme, Ubeequee, and Angelsense consist of battery powered or rechargeable wearables that connect to a 24/7 monitoring support center to alert family members and emergency services if a vulnerable adult is outside designated safe zones. Direct-to-consumer devices, such as Medpage, work similarly, but the information links directly to family members and may not have predefined safety zones or raise an alarm. Connectivity is based on broadband and subject to subscription charges.

Alarms and calls. Technologies such as Tunstall’s MyAmie, Oysta, and Saga’s SOS allow patients to raise an alarm for relatives, caregivers or emergency services with the use of a single button. These technologies often come in the form of a pendant worn around the house and are connected to a hub via a radio signal. The patient can also use the hub to raise an alarm. The pendant must be within reach of the hub for it to work. Other technologies, however, work similarly to the GPS tracker and can rely on broadband for wider network reach. These technologies often also incorporate fall detection and GPS.

Fall detection. Wearables such as Buddi, Telecare, and Careline are designed specifically for dementia care. These use inertia measurement units, gyroscopes, and pressure sensors to detect falls and automatically send messages to caregivers, family members, and first-aid responders. These devices are often accompanied by an alarm button for the user and GPS tracking. Many of these technologies can also be connected to a 24/7 monitoring support team.

Reminders and medication adherence. There are a variety of technologies in this category which allow caregivers to set reminders for patients to take medication, drink water, eat, or remember appointments or social events. Memory aid kits available include the MemRabel care alarm clock with a large screen, connected to a Pivotell Vibratime rechargeable wrist watch that vibrates for reminders. These can be in photo, video or audio format.

The challenge many of these technologies face is that they depend on a caregiver to ensure the patient remembers to engage with and wear the device, charge it when necessary, and crucially, press the button if in distress. In the case of some technologies, they must also be within reach of a hub.

These technologies are good for the early stages of the disease, but as cognitive decline continues, patients will rely more on caregivers to support them, thus limiting their advantages.

In other words, the longevity of these technologies can become incompatible with the patient’s journey, and this is one of the key hurdles to consider when designing and adopting technology in dementia care.

Remote monitoring

This is a fast-growing area for dementia care. Remote monitoring technologies share information on the patient’s daily living patterns with caregivers and family members. The purpose is to provide peace of mind to family members and enable caregivers to make informed care decisions in the short and long term.

Common functions include:

Movement monitoring. Generally delivered by several passive infrared (PIR) sensors installed around the house, and pressure mats in beds and sofas, connected to a hub.
House occupancy. Sensors on external doors to monitor whether an individual has left the house.
Appliance usage. Monitored by connected sensors placed between the mains inlet and the device plug.
Fall detection. Cameras or mmWave radar sensors to detect when an individual has had a fall, without the need for a wearable.

Many of these functions can be delivered by single systems, e.g. Taking Care Home Alert, with the more sophisticated fall detection systems generally targeted at professional care provider users, e.g. Hikvision and Vayyar Care.

It is also common for families to create their own solutions, especially when they feel no existing single solution works for them. This includes the use of consumer tech, such as smartphones, video doorbells, smart home speakers, and cameras around the house. Video doorbells, for example, can be valuable in preventing scams, while smart home speakers can set reminders, automate house functions, or call a relative. However, the use of cameras around the house does pose privacy concerns which need to be considered.

Although the overall objective is to monitor daily independent living, the information often requires interpretation by the caregiver. This can often be facilitated through a dashboard, although the information can be disjointed, and assessment of patterns may not be clear-cut.

Innovator Matt Ash from Supersense Technologies, however, believes we can do more to obtain valuable insights and monitor disease progression efficiently and noninvasively.

“There is a real need for technologies that support caregivers in their role and provide them with the confidence to take a break, knowing their loved one is safe. Though there are some credible assistive technologies out there, the unique needs of families living with dementia are not well served. Projects like the Longitude Prize on Dementia are investing in radical thinking to generate solutions with families living with dementia.”

Talking about some of the latest advancements being tested, Ash continues:

“Everyone’s journey with dementia is different. Right now, we are working on leveraging recent consumer developments in sensor technology, machine learning, and user experience to create personalized assistive systems that can evolve with the needs of an individual with dementia and their caregivers. It’s an incredible opportunity to provide the community with supporting technologies that serve their needs.”

If we want to empower those with dementia to live independently, maximize the impact of caregivers, and provide peace of mind to family members, we must enable the right type of intervention at the right time. Someone with early Alzheimer’s disease may feel overwhelmed or suspicious of new technology, while a person in later stages may be too vulnerable to learn how to use it.

The future of dementia care will center around collecting the right data and extracting the right insights from it to enable better care choices. By allowing technology to provide information on the progression rate of the disease for a particular patient, we can start building a profile of care by recognizing changes in patterns to a baseline. Emerging technologies such as remote monitoring platforms can support this and guide the longevity of other technological interventions to ensure that they align with the individual patient’s journey. At the heart of these technologies, privacy must be a top priority, which may include the use of AI and other methods to allow for patterns to be recognized quickly and with minimal need of human intervention.

We are entering a new era of therapeutics for Alzheimer’s disease, but there is still much to do, particularly in care. Although the use of technology can ultimately support patients, caregivers and family members, it is often incompatible with the individual’s stage of the disease, or inaccessible to caregivers. But as new technologies emerge, data and AI can unlock new insights to support a personalized care plan that scopes each patient to their individual needs – allowing caregivers and families to provide the best care at the right time.

References

E. adult social care insight. The size and structure of the adult social care sector and workforce in England. Technical report, Skills for Care, Workforce Intelligence, 2023.
Alzheimer’s Society, How much does dementia care cost? https://www.alzheimers.org.uk/blog/how-much-does-dementia-care-cost
L. B.-A. A. R. Raphael Wittenberg, Bo Hu. Projections of older people with dementia and costs of dementia care in the United Kingdom, 2019–2040. Technical report, Care Policy and Evaluation Centre, London School of Economics and Political Science, 2019.
B. W. Claudia Miranda-Castillo and M. Orrell. People with dementia living alone: what are their needs and what kind of support are they receiving? International Psychogeriatrics, 2010.
E. adult social care insight. The size and structure of the adult social care sector and workforce in England. Technical report, Skills for Care, Workforce Intelligence, 2023.

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For more on how to accelerate patient-centred innovation in dementia care technology and device design, contact Cambridge Design Partnership.

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The importance of education and innovation in WOC nursing – Reflections on WOCNext 2023

September 1st 2023

Erik Andersen

Insights Research Associate

For 10 years, I’ve been a critical care nurse in the ER and ICU. My colleague, Nicki Sutton, Head of Insights and Strategy and I recently attended the WOCNext 2023 conference in Las Vegas, Nevada – the first I’d been to.

Perhaps a wound, ostomy, and continence (WOC) conference would seem an odd choice for a critical care nurse, devoid of ventilators and vasopressin, and in that light, I guess it is. However, while I am still a practicing ICU nurse, I also work for Cambridge Design Partnership, an end-to-end innovation partner with expertise in product development from idea to production.

So, getting to learn more about the nuanced world of WOC certainly helps inform the clinical perspective I can bring to our clients as we work to help them develop new and novel products to heal patients in a variety of ways.

Our Insights Research Associate, Erik Andersen, at WOCNext2023

As a non-WOC nurse, I must say: I am hugely impressed by the skill and the art that is WOC nursing. Especially, the art of being able to perform astute assessments on skin tones that vary so greatly and being able to ‘read’ the stoma and peristomal skin to overcome the convexity challenges, much like a golfer ‘reads the green’ to sink the putt.

I’ve always respected the knowledge that my WOCN colleagues bring to the table, but frankly, it’s easier to apportion patient care responsibilities in a hospital setting. Even though I try to help with wound care and appliance changes when I can, I don’t always get to appreciate the total value-add they bring to their patients. At WOCNext, it was a different story: the sheer breadth and depth of knowledge that the presenters and attendees possessed was astounding.

Aside from the fact that I still have a lot to learn about wound care, stoma management, and incontinence interventions, there were a number of interesting themes that we were able to pick up on throughout the presentations and discussions:

We’re losing the WOC numbers game

While it’s true of nursing across the board, many folks highlighted the systemic lack of qualified or certified WOC nurses nationally which makes satisfying the needs of their patient populations exceedingly difficult. This scarcity seems to be much more evident in home care settings where resources are already sparse, and now making both access to patient-appropriate products and WOC nurses a huge struggle.

We need to extend education to non-WOC nurses

The second thing that struck me was how much WOC-related knowledge simply doesn’t make it to the bedside nurses, with many people pointing to a lack of education and awareness about wound care best practices and appropriate products held by non-WOC nurses. It seems as though there is an incredible degree of knowledge about these topics that don’t quite reach the broader nursing population, myself very much included.

There’s an opportunity for innovation

As a healthcare innovator, this shows me there is potential opportunity for manufacturers developing healthcare products, specifically those in the wound care and ostomy space, to understand the stark knowledge gap between WOC and non-WOC nurses, and design systems and products that can help upskill non-WOC nurses to offer better coverage of WOC management.

By no means am I suggesting a WOC nurse could be replaced, but rather to the contrary: they’re at a numeric disadvantage and already working hard to cover the patients that need them.

So how can we democratize WOC care so that the lack of available specialists doesn’t have such a negative impact on the patients requiring their care? For instance, maybe there’s room to lean on a technology aid to remove some of the subjectivity that an artful WOC nurse can account for, but that the less specialized nurses among us, like me, can leverage to do a better job with assessments or appliance management.

That’s the kind of work we can support at CDP, where we have been innovating in the healthcare industry for over 25 years. We have experts across the product development pathway: from patient and clinician research, to design and engineering, to usability testing and validation, all blanketed under our ISO 9001 and 13485 certifications for medical device development and manufacture.

We aim to improve lives through innovation much like our clinical colleagues, aim to improve lives through healthcare delivery. One such example is the work we’ve done with ostomy product innovation start-up, Ostique. Over four months, we supplied the technical rigor to help them develop a concept for a unique ostomy appliance and build prototypes of the device for user feedback. Today, having successfully brought their product to market, Ostique’s revolutionary pouch covers are available in a range of colors and skin tones to meet patients’ needs.

The need for improved healthcare and delivery is there – we could see it everywhere at WOCNext 2023 – and we’re here to help you address it. To discuss how we can help your organization take your product to market, chat to the team today.

Neurodegenerative conditions: turning a corner to better treatment?

June 21st 2023

Dr Karla Sanchez

Head of Biomedical Engineering

Pace is accelerating for tackling neurodegenerative diseases. Can we unlock better treatment? Can we reach a cure?

Ageing populations face neurodegenerative conditions, such as Alzheimer’s Disease, Parkinson’s Disease, Motor Neurone Disease, Multiple Sclerosis, and others. These impact an estimated 60 million people worldwide, equivalent to the current UK population.

Whilst each condition has different mechanisms of neurodegeneration, they all have something in common: prognosis is bleak, treatment is limited, and there is no cure.

However, after decades of research, there has been a series of breakthroughs. Here, we focus on two areas of progress: how treatments have moved on and hope for the future.

The rise of RNA-based therapeutics

The effective development of RNA-based vaccines during the COVID-19 outbreak catapulted RNA-based therapeutics into the spotlight. Whilst theoretical knowledge of RNA therapy has existed for over 30 years, the bulk of associated FDA approval for treatments involving the nervous system has occurred in the last decade⁽¹⁾.

A major advantage of RNA-based therapy over conventional small molecule and protein-based approaches is its high specificity and precision, resulting in a more targeted approach to treating disease with specific gene mutations or overexpression.

However, to devise effective RNA-based therapeutics, the genetic hallmarks of the neurodegenerative disease of interest must be known.

Motor Neurone Disease (MND) is one such condition where specific mutations in the SOD1 gene have been identified and in this case, in two per cent of diagnosed cases.

A recent breakthrough in phase three clinical trials targeted this gene using the drug Tofersen. Tofersen, developed by Biogen, directly interferes with the faulty overproduction of SOD1. After six months, patients had a reduction in SOD1 levels, and after 12 months the same patients reported better mobility and lung function^(2,3). Although patients with SOD1 mutations only represent two per cent of those living with MND, these trials provide ‘proof of concept’ that similar gene therapy-based approaches may help other forms of the disease.

Another pioneering strategy, developed by Atalanta Therapeutics and Genentech, focuses on a technology called branched siRNA (small interference RNA). This is a type of molecule that helps regulate gene expression by binding to a complementary messenger RNA, which in turn can encode the gene of interest.

Branched siRNA uses novel RNA interference nucleotide technology to suppress the activity of genes that function abnormally, such as mutations. This slows the progression of the disease or stops it altogether.

It is hoped this approach can be applied across multiple neurodegenerative diseases, including Parkinson’s Disease, Huntington’s Disease and Alzheimer’s Disease.

Although testing is still in the pre-clinical stage, the branched siRNA platform aims to enable RNA interference to be deployed as a therapeutic approach throughout the brain and spinal cord. This overcomes the long-standing challenge of achieving adequate distribution within the central nervous system (CNS) to ensure the therapeutic agent reaches the nervous tissue^(4,5).

Progress in non-RNA therapeutics

Non-RNA therapeutics for neurodegenerative conditions also continue to progress. Examples include the monoclonal antibody Donanemab, developed by Eli Lilly. Phase three clinical trials showed it to slow clinical decline by 35% in patients with Alzheimer’s Disease, compared to a placebo⁽⁶⁾.

Effective delivery remains a major challenge

One of the main challenges in developing RNA therapeutics, and therapeutics for the brain in general, remains the efficiency of its delivery to the target tissue.

To treat neurodegenerative conditions, the therapeutic agent aims to reach the CNS. The presence of the blood-brain barrier (BBB), a cell-formed wall separating the bloodstream and the CNS, makes it difficult to deliver drugs. The BBB’s almost impermeable characteristics allow very few molecules to cross and make systemic drug delivery less efficacious.

There are two common approaches to overcome this: re-engineering the therapeutic agent to make it compatible with BBB permeability or bypassing the BBB altogether.

Re-engineering the therapeutic agent

This typically involves chemical modification of the drug (e.g., from water-soluble to lipid-soluble molecules) to enable passive diffusion through the BBB. Another approach is to design drug carriers that mimic the structure of endogenous molecules (e.g., monosaccharides, hormones) to activate carrier-mediated transport or nanocarriers^(7,8). Both approaches add complexity to manufacturing.

Another cross-BBB approach is Focused Ultrasound (FUS), where high-intensity sound waves temporarily disrupt the BBB to enable drug-loaded microbubbles to enter the CNS⁹.

Bypassing the blood-brain barrier

Bypassing the BBB can save time and effort in formulation by using a range of therapeutic agents not constricted by size or BBB compatibility. Of its three most common types of delivery: intraparenchymal, intranasal, and cerebrospinal fluid (CSF) delivery; the latter is often the favored approach, due to lower clinical complexity¹⁰.

Evaluating CSF delivery routes

CSF delivery most commonly include intrathecal (IT) or intraventricular (ICV) routes.

IT involves an injection either on the lumbar or a cisterna magna region to deliver the drug and let CSF pulsatile flow support the distribution of the therapeutic agent in the brain and spinal cord.

ICV is more invasive. It involves two surgical interventions, one to place a catheter connecting the cerebral ventricles to the injection port at the top of the skull and one to remove the catheter.

To date, ICV has two approved drugs (Rituxan for CNS Lymphoma, and Brineura for Neuronal Ceroid Lipofuscinoses type two). IT lumbar injection has one (Spiranza for Spinal Muscular Atrophy) and plenty more in clinical and pre-clinical stages across a spectrum of neurodegenerative and neurological diseases⁽¹¹⁾. Irrespective of the approach, the trend is clear: less invasive, lower dosage, and targeted delivery is the way to go.

In the race to show safety and efficacy with either invasive or non-invasive approaches, all solutions will have to be patient-centered.

A new dawn for the treatment of neurodegenerative diseases

The complexities of neurodegeneration have long frustrated scientists and clinicians alike, despite decades dedicated to studying its diseases, aetiologies, and treatments. However, we are making more rapid and more significant progress.

We have some way to go, but we mustn’t overlook the magnitude of these milestones. New therapeutics and delivery techniques are paving the way to more effective and efficient treatment.

By increasing our understanding of genetic hallmarks of the diseases, and using tools such as AI in drug discovery, we can unlock faster pathways to RNA-based treatments. Similarly, by finding innovative ways of demonstrating the safety and efficacy of delivery methods, such as modeling, we can edge closer to less invasive procedures and lower dosages to minimize potential side effects.

We need more research, more awareness, earlier diagnosis, and a better understanding of risk factors to enable prevention and earlier intervention.

But we are now getting closer to better treatment and one day finding a cure.

References

http://nectar.northampton.ac.uk/16015/1/Anthony_Karen_RNAB_2022_RNA_based_therapeutics_for_neurological_diseases.pdf
https://www.sheffield.ac.uk/neuroscience-institute/news/promising-mnd-drug-helps-slow-disease-progression-and-benefits-patients-physically
https://www.nejm.org/doi/full/10.1056/NEJMoa2204705
https://www.gene.com/stories/pioneering-novel-therapeutics-in-neuroscience
https://www.nature.com/articles/s41587-019-0205-0
https://clinicaltrials.gov/ct2/show/NCT04437511?term=TRAILBLAZER-ALZ&cond=Alzheimer+Disease&draw=2&rank=3
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8905930/
https://ijponline.biomedcentral.com/articles/10.1186/s13052-018-0563-0#:~:text=Modification%20of%20the%20drug%20to,capable%20of%20crossing%20the%20BBB.
https://clinicaltrials.gov/ct2/show/NCT03321487
https://www.frontiersin.org/articles/10.3389/fnagi.2019.00373/full
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9305158/

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For more on how to advance RNA therapeutics and targeted CNS drug delivery for neurodegenerative diseases, contact Cambridge Design Partnership.

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